Search Results (2,377)

Eddy current internal inspection technology has been extensively studied and applied in industrial environments such as oil and gas pipelines. Typically, the penetration depth of eddy current testing in ferromagnetic pipes is reduced compared to non-ferromagnetic materials due to their high magnetic permeability. Given the presence of noise interference in eddy current testing systems, enhancing the signal quality of receiver coils constitutes a critical research focus. This paper proposes and validates through simulations a non-contact magnetically focused eddy current testing (MFECT) system for pipeline internal inspection. The system effectively enhances the signal-to-noise ratio of the receiver coils, mitigates the skin effect, and increases the penetration depth. Comparative analyses of penetration depth with and without magnetic focusing were conducted. Furthermore, the optimal operating frequency of the excitation signal was determined through simulation studies. Finally, the feasibility of the proposed method was verified through experiments. Full article

Fiber-optic sensing (FOS) technology has emerged as a cutting-edge research focus in the sensor field due to its miniaturized structure, high sensitivity, and remarkable electromagnetic interference immunity. Compared with conventional sensing technologies, FOS demonstrates superior capabilities in distributed detection and multi-parameter multiplexing, thereby accelerating its applications across biomedical, industrial, and aerospace fields. This paper conducts a systematic analysis of the sensing mechanisms in fiber-optic pressure sensors, with a particular focus on the performance optimization effects of fiber structures and materials, while elucidating their application characteristics in different sensing scenarios. This review further examines current manufacturing technologies for fiber-optic pressure sensors, covering key processes including fiber processing and packaging. Regarding practical applications, the multifunctional characteristics of fiber-optic pressure sensors are thoroughly investigated in various fields, including biomedical monitoring, industrial and energy monitoring, and wearable devices, as well as aerospace monitoring. Furthermore, current challenges are discussed regarding performance degradation in extreme environments and multi-parameter cross-sensitivity issues, while future research directions are proposed, encompassing the integration and exploration of novel structures and materials. By synthesizing recent advancements and development trends, this review serves as a critical reference bridging the gap between research and practical applications, accelerating the advancement of fiber-optic pressure sensors. Full article

Accurate bearing fault diagnosis under various operational conditions presents significant challenges, mainly due to the limited availability of labeled data and the domain mismatches across different operating environments. In this study, an adaptive meta-learning framework (AdaMETA) is proposed, which combines dynamic task-aware model-independent meta-learning (DT-MAML) with efficient multi-scale attention (EMA) modules to enhance the model’s ability to generalize and improve diagnostic performance in small-sample bearing fault diagnosis across different load scenarios. Specifically, a hierarchical encoder equipped with C-EMA is introduced to effectively capture multi-scale fault features from vibration signals, greatly improving feature extraction under constrained data conditions. Furthermore, DT-MAML dynamically adjusts the inner-loop learning rate based on task complexity, promoting efficient adaptation to diverse tasks and mitigating domain bias. Comprehensive experimental evaluations on the CWRU bearing dataset, conducted under carefully designed cross-domain scenarios, demonstrate that AdaMETA achieves superior accuracy (up to 99.26%) and robustness compared to traditional meta-learning and classical diagnostic methods. Additional ablation studies and noise interference experiments further validate the substantial contribution of the EMA module and the dynamic learning rate components. Full article

In extra-large spaces, the varying distances between distributed loudspeakers and listeners lead to sound delays in the loudspeakers’ concentrated projection areas. When combined with the inherent long-delay reflected sounds in those spaces, this dual effect exacerbates the echo problems and poses challenges to maintaining speech intelligibility. To explore the influence mechanism of echo interference on speech intelligibility in extra-large spaces, a questionnaire survey was carried out in two representative extra-large buildings, and then listening experiments were conducted in the laboratory under different echo conditions and impulse characteristics. The results highlighted that (1) apparent echo problems existed in extra-large spaces and severely affected speech intelligibility; (2) the echo phenomenon can be classified into three groups—no echo (0 ms), short delay (100 or 200 ms), and long delay (≥300 ms)—with the detrimental effect on intelligibility increasing across the groups; and (3) a curve was established to describe the relationship between speech intelligibility and STI in extra-large spaces, and compared with the standard curve, the STI thresholds require further adjustment. These findings indicate that echoes in extra-large spaces significantly impair speech intelligibility and reduce the accuracy of its prediction, and therefore should not be neglected. Full article

Electromagnetic interference (EMI) represents a growing challenge in the modern era, as electronic systems and wireless technologies become increasingly integrated into daily life. This review provides a comprehensive overview of EMI, beginning with its historical evolution over centuries, from early power transmission systems and industrial machinery to today’s complex environment shaped by IoT, 5G, smart devices, and autonomous technologies. The diverse sources of EMI and their wide-ranging effects are examined, including disruptions in electrical and medical devices, ecological impacts on wildlife, and potential risks to human health. Beyond its technical and societal implications, the economic dimension of EMI is explored, highlighting the rapid expansion of the global shielding materials market and its forecasted growth driven by telecommunications, automotive, aerospace, and healthcare sectors. Preventative strategies against EMI are discussed, with particular emphasis on the role of advanced materials. Carbon-based nanomaterials—such as graphene, carbon nanotubes, and carbon foams—are presented as promising solutions owing to their exceptional conductivity, mechanical strength, tunable structure, and environmental sustainability. By uniting perspectives on EMI’s origins, consequences, market dynamics, and mitigation strategies, this work underscores the urgent need for scalable, high-performance, and eco-friendly shielding approaches. Special attention is given to recent advances in carbon-based nanomaterials, which are poised to play a transformative role in ensuring the safety, reliability, and sustainability of future electronic technologies. Full article

At present, the development of power cables shows three notable trends: higher voltage, longer distance and more complex environments. Against this backdrop, the limitations of traditional detection techniques in locating local defects have become increasingly apparent. Frequency Domain Reflectometry (FDR) has garnered sustained research attention both domestically and internationally due to its high sensitivity and accuracy in detecting localized defects. This paper aims to compare the defect localization effectiveness of the Fast Fourier Transform (FFT) method and the Inverse Fast Fourier Transform (IFFT) method within FDR. First, the differences between the two methods are analyzed from a theoretical perspective. Then, field tests are conducted on cables of varying voltage levels and lengths, with comparisons made using parameters such as full width at half maximum (FWHM) and signal-to-noise ratio (SNR). The results indicate that the FFT method is more suitable for low-interference or short cables, while the IFFT method is more suitable for high-noise, high-resolution, or long cables. Full article

To date, visual analysis is mainly used to evaluate images of dehydrated films (facies) of biological fluids—microscopy at various magnifications, illumination with white or polarized light, as well as using a dark field. At the same time, important information on the architectonics of optically anisotropic supramolecular networks of facies is unknown (inaccessible). In our work, a model of optical anisotropy of the architectonics of supramolecular networks of blood facies is proposed. Algorithms and a methodology for a new multifunctional method of polarization-interference visualization of the Jones matrix and digital layer-by-layer phase reconstruction of optical anisotropy maps (theziograms) have been developed. As a result, statistically significant markers of oncological changes in the polycrystalline architectonics of supramolecular networks of blood facies samples from healthy donors and patients with papillary thyroid cancer at different stages of the oncological process have been determined and physically analyzed. A comparative study of the diagnostic efficiency of Jones matrix theziography (JT) and Mueller matrix diffusion tomography (MDT) of blood facies samples was conducted within the framework of evidence-based medicine. The main advantages of the Jones matrix method are shown: its multifunctionality (complex detection of birefringence and dichroism), high accuracy of early (stage 1: JM—90.4% and MDT—78.8%) and current (stage 2: JM—96.2% and MDT—88.5%) cancer diagnostics and an excellent level (JM—94.2% and MDT—84.6%) of differentiation of papillary thyroid cancer stages. Full article

Alzheimer’s disease (AD) requires early and accurate identification of affected brain regions, which can be achieved through the detection of specific biomarkers to enable timely intervention. Carbon nanomaterials (CNMs), including graphene derivatives, carbon nanotubes, graphitic carbon nitride, carbon black, fullerenes, and carbon dots, offer high conductivity, large electroactive surface area, and versatile surface chemistry that enhance biosensor performance. While such properties benefit a wide range of transduction principles (e.g., electrochemical, optical, and plasmonic), this review focuses on their role in electrochemical biosensors. This review summarizes CNM-based electrochemical platforms reported from 2020 to mid-2025, employing aptamers, antibodies, and molecularly imprinted polymers for AD biomarker detection. Covered topics include fabrication strategies, transduction formats, analytical performance in complex matrices, and validation. Reported devices achieve limits of detection from the femtomolar to picogram per milliliter range, with linear ranges typically spanning 2–3 orders of magnitude (e.g., from femtomolar to picomolar, or from picogram to nanogram per milliliter levels). They exhibit high selectivity against common interferents such as BSA, glucose, uric acid, ascorbic acid, dopamine, and non-target peptides, along with growing capabilities for multiplexing and portable operation. Remaining challenges include complex fabrication, limited long-term stability and reproducibility data, scarce clinical cohort testing, and sustainability issues. Opportunities for scalable production and integration into point-of-care workflows are outlined. Full article

Background: This study aimed to evaluate the influence of scleral contact lens (SCL) wear on optical coherence tomography (OCT) scan quality and structural measurements in patients with keratoconus. Methods: This retrospective observational study included 28 eyes of 28 keratoconus patients. All participants underwent a comprehensive ophthalmologic evaluation, including corneal topography and spectral-domain OCT (Optopol REVO 60). Two OCT measurement sessions were performed on the same day: one without SCLs and one after a 30–75 min adaptation period with Mini Misa^® scleral lenses. Recorded parameters included corneal and epithelial thicknesses, ganglion cell–inner plexiform layer (GCIPL) thickness, retinal nerve fiber layer (RNFL) thickness, and device-reported quality index (QI). Correlation analyses between topographic values, age, and OCT parameters were also conducted. Results: The mean age of participants was 32.96 ± 13.72 years. SCL wear significantly decreased anterior segment QI (6.76 ± 1.73 vs. 5.57 ± 2.34, p = 0.019) but improved posterior segment QI in both the ganglion (2.52 ± 1.03 vs. 5.76 ± 2.17, p < 0.001) and disc (2.82 ± 0.94 vs. 4.39 ± 1.87, p < 0.001) modules. Central corneal thickness remained stable, while central epithelial thickness decreased slightly (50.53 ± 6.66 µm vs. 47.59 ± 7.20 µm, p = 0.007). RNFL and GCIPL thicknesses showed no significant changes, except for minor sectoral variations. Steeper keratometry values correlated with lower QI in both conditions. Conclusions: SCLs enhanced posterior OCT scan quality while reducing anterior segment image clarity. These findings suggest that SCLs not only provide visual rehabilitation but also facilitate more reliable posterior segment imaging in keratoconus patients, despite mild interference with anterior segment OCT metrics. Further prospective studies are warranted to validate these results. Full article

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis. Full article

RNA interference (RNAi) is a powerful mechanism of post-transcriptional gene regulation in which small interfering RNA (siRNA) is utilized to target and degrade specific RNA sequences. In this study, experiments were conducted to evaluate the efficacy of combination siRNA therapy against enterovirus 71 (EV71) and the potential of this therapy to delay or prevent the emergence of resistance in vitro. siRNAs targeting multiple genes of EV71 were designed, and the effects of a cocktail of siRNAs on viral replication were assessed compared to those of single-siRNA treatment. Cotransfection of multiple siRNAs targeting different protein-coding genes of the EV71 genome effectively suppressed escape mutants resistant to RNAi. Combination therapy with siRNAs targeting multiple viral genes successfully prevented viral escape mutations over five passages. By contrast, serial passaging with a single siRNA led to the rapid emergence of resistance, with mutations identified in the siRNA target sites. The combination of siRNAs specifically targeting different regions demonstrated an additive effect and was more effective than individual siRNAs at inhibiting EV71 replication. This study supports the effectiveness of combination therapy using siRNAs targeting multiple genes of EV71 to inhibit viral replication and prevent the emergence of resistant escape mutants. Overall, the findings identify RNAi targeting multiple viral genes as a potential strategy for therapeutic development against viral diseases and for preventing the emergence of escape mutants resistant to antiviral RNAi. Full article

Fusobacterium nucleatum (Fn) has been increasingly recognized as a crucial mediator of colorectal cancer (CRC) biology, particularly in microsatellite-stable (MSS) tumors, where immune checkpoint inhibitors (ICIs) have shown limited efficacy. Rather than representing a passive microbial passenger, Fn actively shapes tumor behavior by adhering to epithelial cells, activating oncogenic signaling, and promoting epithelial–mesenchymal transition (EMT). At the same time, it remodels the tumor microenvironment, driving immune suppression through inhibitory receptor engagement, accumulation of myeloid-derived cells, and metabolic reprogramming of tumor-associated macrophages. These mechanisms converge to impair cytotoxic immunity and contribute to both intrinsic and acquired resistance to ICIs. Beyond immune escape, Fn interferes with conventional chemotherapy by sustaining autophagy and blocking ferroptosis, thereby linking microbial colonization to multidrug resistance. Most of these mechanisms derive from preclinical in vitro and in vivo models, where causal relationships can be inferred. In contrast, human data are mainly observational and provide correlative evidence without proving causality. No interventional clinical studies directly targeting Fn have yet been conducted. Its enrichment across the adenoma–carcinoma sequence and consistent detection in both tumor and fecal samples highlight its potential as a biomarker for early detection and patient stratification. Importantly, multidimensional stool assays that integrate microbial, genetic, and epigenetic markers are emerging as promising non-invasive tools for CRC screening. Therapeutic strategies targeting Fn are also under exploration, ranging from antibiotics and bacteriophages to multifunctional nanodrugs, dietary modulation, and natural microbiota-derived products. These approaches may not only reduce microbial burden but also restore immune competence and enhance the efficacy of immunotherapy in MSS CRC. Altogether, current evidence positions Fn at the intersection of microbial dysbiosis, tumor progression, and therapy resistance. A deeper understanding of its pathogenic role may support the integration of microbial profiling into precision oncology frameworks, paving the way for innovative diagnostic and therapeutic strategies in CRC. Full article

As the environmental regulations of the International Maritime Organization (IMO) become more stringent, the accurate prediction of ship propulsion performance has become essential. Under ballast conditions where the draft is shallow, the propeller approaches the free surface, causing complex phenomena such as ventilation and surface piercing, which reduce propulsion efficiency. The conventional virtual disk (VD) method cannot adequately capture these free-surface effects, leading to deviations from model propeller results. To resolve this, a correction formula that accounts for the advance ratio (J) and submergence ratio ($h/D$) has been proposed in previous studies. In this study, the correction formula was simplified and implemented in a CFD environment using a field function, enabling dynamic adjustment of body force based on time-varying submergence depth. A comparative analysis was conducted between the conventional VD, modified VD, and model propeller using POW and self-propulsion simulations for an MR tanker and SP598M propeller. The improved method was validated in calm and regular wave conditions. The results showed that the modified VD method closely matched the performance trends of the model propeller, especially in free surface-interference conditions (e.g., $h/D$ < 0.5). Furthermore, additional validations in wave-induced self-propulsion confirmed that the modified VD method accurately reproduced the reductions in wake fraction and thrust deduction coefficient, unlike the overestimations observed with the conventional VD. These results demonstrate that the modified VD method can reliably predict propulsion performance under real sea states and serve as a practical tool in the early design stage. Full article

With the continuous advancement of 3D perception technology, point cloud data has found increasingly widespread application. However, the presence of holes in point cloud data caused by device limitations and environmental interference severely restricts algorithmic performance, making point cloud completion a research topic of high interest. This study observes that most existing mainstream point cloud completion methods primarily focus on capturing global features, while often underrepresenting local structural details. Moreover, the generation process of complete point clouds lacks effective control over fine-grained features, leading to insufficient detail in the completed outputs and reduced data integrity. To address these issues, we propose a Set Combination Multi-Layer Perceptron (SCMP) module that enables the simultaneous extraction of both local and global features, thereby reducing the loss of local detail information. In addition, we introduce the Squeeze Excitation Pooling Network (SEP-Net) module, an informative channel attention mechanism capable of adaptively identifying and enhancing critical channel features, thus improving the overall feature representation capability. Based on these modules, we further design a novel Feature Fusion Point Fractal Network (FFPF-Net), which fuses multi-dimensional point cloud features to enhance representation capacity and progressively refines the missing regions to generate a more complete point cloud. Extensive experiments conducted on the ShapeNet-Part and MVP datasets compared to L-GAN and PCN showed average prediction error improvements of 1.3 and 1.4, respectively. The average completion errors on the ShapeNet-Part and MVP datasets are 0.783 and 0.824, highlighting the improved fine-detail reconstruction capability of our network. These results indicate that the proposed method effectively enhances point cloud completion performance and can further promote the practical application of point cloud data in various real-world scenarios. Full article

Molecularly imprinted polymer (MIP) biosensors offer an attractive strategy for selective biomolecule detection, yet imprinting proteins with structural fidelity remains a major challenge. In this work, we present a rationally designed electrochemical biosensor for matrix metal-loproteinase-8 (MMP-8), a key salivary biomarker of periodontal disease. By integrating graphene oxide (GO) with electropolymerized poly(eriochrome black T, EBT) films on screen-printed carbon electrodes, the partially reduced GO interface enhanced electrical conductivity and facilitated the formation of well-defined poly(EBT) films with re-designed polymerization route, while template extraction generated artificial antibody-like sites capable of specific protein binding. The MIP-based electrodes were comprehensively validated through morphological, spectroscopic, and electrochemical analyses, demonstrating stable and selective recognition of MMP-8 against structurally similar interferents. Complementary density functional theory (DFT) modeling revealed energetically favorable interactions between the EBT monomer and catalytic residues of MMP-8, providing molecular-level insights into imprinting specificity. These experimental and computational findings highlight the importance of rational monomer selection and nanomaterial-assisted polymerization in achieving selective protein imprinting. This work presents a systematic approach that integrates electrochemical engineering, nanomaterial interfaces, and computational validation to address long-standing challenges in protein-based MIP biosensors. By bridging molecular design with practical sensing performance, this study advances the translational potential of MIP-based electrochemical biosensors for point-of-care applications. Full article